Publication details

Solving Linear Pseudo-Boolean Constraint Problems with Local Search

Joachim Paul Walser

Proceedings of the fourteenth National Conference on Artificial Intelligence (AAAI '97), 1997

Stochastic local search is one of the most successful
methods for model finding in propositional satisfiability.
However, many combinatorial problems have no concise
propositional encoding. In this paper, we show that
domain-independent local search for satisfiability (Walksat)
can be generalized to handle systems of linear
pseudo-Boolean (0-1 integer) constraints, a representation
that is widely used in operations research. We introduce
the algorithm WSAT(PB) and demonstrate its potential in two
case studies. The first application is an optimization
problem from radar surveillance. Experiments on problems of
realistic size show that WSAT(PB) is an efficient heuristic
to find good approximate solutions. For most of the test
problems, it found provably optimal solutions. In the
second case study, we show that pseudo-Boolean local search
can efficiently solve the progressive party problem, a
problem that is hard for constraint programming with
chronological backtracking, and whose 0-1 encoding is beyond
the size limitations of integer linear programming.